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```
## `Agent`with Pydantic BaseModel as Output Type
The following is an example of an agent that intakes a pydantic basemodel and outputs it at the same time:
```python
from pydantic import BaseModel, Field
from swarms import Anthropic
from swarms import Agent
# Initialize the schema for the person's information
class Schema(BaseModel):
name: str = Field(..., title="Name of the person")
agent: int = Field(..., title="Age of the person")
is_student: bool = Field(..., title="Whether the person is a student")
courses: list[str] = Field(
..., title="List of courses the person is taking"
)
# Convert the schema to a JSON string
tool_schema = Schema(
name="Tool Name",
agent=1,
is_student=True,
courses=["Course1", "Course2"],
)
# Define the task to generate a person's information
task = "Generate a person's information based on the following schema:"
# Initialize the agent
agent = Agent(
agent_name="Person Information Generator",
system_prompt=(
"Generate a person's information based on the following schema:"
),
# Set the tool schema to the JSON string -- this is the key difference
tool_schema=tool_schema,
llm=Anthropic(),
max_loops=3,
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
interactive=True,
# Set the output type to the tool schema which is a BaseModel
output_type=tool_schema, # or dict, or str
metadata_output_type="json",
# List of schemas that the agent can handle
list_tool_schemas=[tool_schema],
function_calling_format_type="OpenAI",
function_calling_type="json", # or soon yaml
)
# Run the agent to generate the person's information
generated_data = agent.run(task)
# Print the generated data
print(f"Generated data: {generated_data}")
```
### `SwarmNetwork`
`SwarmNetwork` provides the infrasturcture for building extremely dense and complex multi-agent applications that span across various types of agents.

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